Code for Computer Science

Constrained Engineering Optimization Problems In this video, we applied different Metaheuristic Optimization Algorithms on 3 different Constrained Engineering Design Optimization Problems E01, E02 and E03. E01: Welded beam design problem. E02: Speed Reducer design optimization problem. E03: Tension/Compression spring design optimization problem. All constrained engineering optimization problems have different Objective function, Decision variables and Constraints. We did not try to optimize SSA parameters, for each problem constraints are directly handled [it means IF Solution can not satisfy the constraints – we will consider it Infeasible Solution]. Three engineering problems are solved using Sparrow Search Algorithm (SSA). We also compared the results with respect to 3 Metaheuristic Algorithms: Particle Swarm Optimization Algorithm (PSO), Grey Wolf Optimization Algorithm (GWO) and Teaching Leaning Based Optimization Algorithm (TLBO). When we compared SSA with other algorithms, the p

Metaheuristic Optimization Algorithms in Big Data, Web Mining and Text Clustering. Metaheuristic optimization algorithms are best swarm intelligence methods and widely used today in Big Data, Web Mining AND Text clustering. Using metaheuristic optimization algorithms we can solve complex Machine Learning problems.  Clustering: Clustering is a common text mining technique. We can used clustering technique for the representation of Dataset that contain similarities between objects. We can use clustering in Web mining, Image Processing, Sentiment Analysis, Data Clustering, Text document clustering, and Text classification. Clustering technique is classified into 3 classes: 1.) Overlapping 2.) Partitioning 3.) Hierarchical  In Partitioning Process we can use metaheuristic optimization approaches. Partitioning process is used for the transformation of any given problem into optimization problem. Partitioning process is based on either minimization or maximization. Partitioning methods are a

Bacterial Foraging Optimization Algorithm (BFOA)  Bacterial Foraging Optimization Algorithm is a recently developed nature-inspired optimization algorithm, which is based on the foraging behavior of Escherichia coli or E. coli bacteria. Bacterial Foraging Optimization Algorithm Advantages: 1.) Used to solve Engineering Problems. 2.) Used to Solve complex real world Optimization Problems. About Escherichia coli or E. coli bacteria. Escherichia coli or E. coli bacteria lives in our intestine and they are also found in the gut of some animals. Most of the Escherichia coli or E. coli bacteria are harmless. But some can cause Diarrhoea, if you eat contaminated food or drink fouled water. Escherichia coli or E. coli bacteria is mainly associated with Food positioning, Urinary Tract Infection (UTI) - approximate 75%-95% UTI are caused by Escherichia coli or E. coli bacteria. Escherichia coli or E. coli bacteria causes certain symptom's: Vomiting's, Confusion, Diarrhoea, Abdominal Cram